Lecture

Neural Network Quantum State Tomography

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In course

PHYS-754: Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the v ...

Description

This lecture covers the application of neural networks for quantum state tomography, focusing on highly-entangled quantum systems. It explains the use of artificial neural networks to approximate many-body wave functions, training schemes, and the reconstruction accuracy. The lecture also discusses the ML approach to quantum state tomography, the RBM models, and the overfitting phenomenon in powerful models. Furthermore, it explores the implementation of QST on W states and genuine many-body problems, including the comparison with quantum Monte Carlo simulations.

Instructors (10)

Anne-Clémence Corminboeuf

Teaching & PhD PhD Students Liam Oliver Marsh, Pratham Pushparaj Deshmukh, Roel Christiaan M Raike, Thanapat Worakul, Osvaldo Hernandez Cuellar, Juliette Veronique Schleicher, Yannick Calvino Alonso, Sara Bassetta, Yuri Cho, Kelian Dietmar Marc Gaedecke, Guoyuan Liu Past EPFL PhD Students Fabrice Avaltroni, Daniel Filipe Jana, Stephan Niklaus Steinmann, Jérôme Florian Gonthier, Riccardo Petraglia, Antonio Prlj, Laurent Alexis Clément Vannay, Alberto Fabrizio, Kun-Han Lin, Boodsarin Sawatlon, Raimon Fabreg

Philippe Schwaller

Michele Ceriotti

Michele Ceriotti received his Ph.D. in Physics from ETH Zürich in 2010. He spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 he leads the laboratory for Computational Science and Modeling in the Institute of Materials at EPFL. His research revolves around the atomic-scale modelling of materials, based on the sampling of quantum and thermal fluctuations and on the use of machine learning to predict and rationalize structure-property relations. He has been awarded the IBM Research Forschungspreis in 2010, the Volker Heine Young Investigator Award in 2013, an ERC Starting Grant in 2016, and the IUPAP C10 Young Scientist Prize in 2018.

David Richard Harvey

Expertise CosmologyDark MatterGalaxy ClustersStrong Gravitational LensingWeak Gravitational Lensing I am an observational and theoretical astrophysicist at EPFL. My main area of research is dark matter and trying to understand its properties and dynamics. I love to use deep learning in my work and understand how fondation models and generative AI can help us understand the Universe. I am a member of Euclid and ARRAKIHS and the lead of the self interacting dark matter Key Project in Euclid. Outside of my r

Matthieu Wyart

Teaching & PhD Past EPFL PhD Students Riccardo Ravasio, Mario Geiger, Wencheng Ji, Leonardo Petrini, Umberto Maria Tomasini, Alessandro Favero Courses Systèmes complexes PHYS-343 This courses show how known (entropy, phase transitions) and new (glasses, percolation, avalanche-type response) physical concepts can be used in various fields (including Computer science, neuroscience, social sciences).

Official source

Ontological neighbourhood

Theoretical computer science

Theory of computation: Quantum computing

Related concepts (30)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Neural network

A neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.

Qubit

In quantum computing, a qubit (ˈkjuːbɪt) or quantum bit is a basic unit of quantum information—the quantum version of the classic binary bit physically realized with a two-state device. A qubit is a two-state (or two-level) quantum-mechanical system, one of the simplest quantum systems displaying the peculiarity of quantum mechanics. Examples include the spin of the electron in which the two levels can be taken as spin up and spin down; or the polarization of a single photon in which the two states can be taken to be the vertical polarization and the horizontal polarization.

Quantum entanglement

Quantum entanglement is the phenomenon that occurs when a group of particles are generated, interact, or share spatial proximity in a way such that the quantum state of each particle of the group cannot be described independently of the state of the others, including when the particles are separated by a large distance. The topic of quantum entanglement is at the heart of the disparity between classical and quantum physics: entanglement is a primary feature of quantum mechanics not present in classical mechanics.

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